Intrinsic Complexity Research Articles

Validated Multimethod Approach for Full Characterization of 2'-Fucosyl-d-lactose as an Industrially Produced Human Milk Oligosaccharide.

Human milk oligosaccharides are of high interest as active ingredients in infant formulas and dietary food supplements. Full characterization of members of this compound class is challenging due to the intrinsic complexity of byproducts during synthesis by fermentation. Moreover, when method validation is targeted for a regulated environment, a robust chromatographic separation of the highly polar oligosaccharides needs to be addressed, including isomers and compounds relevant for potential product adulteration. We present a combined approach of validated chromatography and NMR spectroscopy, which allows for full mass balancing of industrially produced 2'-fucosyl-d-lactose. A combination of NMR spectroscopy, mass spectrometry, and action IR spectroscopy tackles structural elucidation of monoacetylated species as a new class of byproducts.

Global Optimization of Molybdenum Subnanoclusters on Graphene: A Consistent Approach toward Catalytic Applications.

The development of novel subnanometer clusters (SNCs) catalysts with superior catalytic performance depends on the precise control of clusters' atomistic sizes, shapes, and accurate deposition onto surfaces. The intrinsic complexity of the adsorption process complicates the ability to achieve an atomistic understanding of the most relevant structure-reactivity relationships hampering the rational design of novel catalytic materials. In most cases, existing computational approaches rely on just a few structures to draw conclusions on clusters' reactivity thereby neglecting the complexity of the existing energy landscapes thus leading to insufficient sampling and, most likely, unreliable predictions. Moreover, modeling of the actual experimental procedure that is responsible for the deposition of SNCs on surfaces is often not done even though in some cases this procedure may enhance the significance of certain (e.g., metastable) adsorption geometries. This study proposes a novel systematic approach that utilizes global search techniques, specifically, the particle swarm optimization (PSO) method, in conjunction with ab initio calculations, to simulate all stages in the beam experiments, from predicting the most relevant SNCs structures in the beam and on a surface, to their reactivity. To illustrate the main steps of our approach, we consider the deposition of Molybdenum SNC of 6 Mo atoms on a free-standing graphene surface, as well as their catalytic properties with respect to the CO molecule dissociation reaction. Even though our calculations are not exhaustive and serve only to produce an illustration of the method, they are still able to provide insight into the complicated energy landscape of Mo SNCs on graphene demonstrating the catalytic activity of Mo SNCs and the importance of performing statistical sampling of available configurations. This study establishes a reliable procedure for performing theoretical rational design predictions.

A comprehensive review of immune checkpoint inhibitor-related diabetes mellitus: incidence, clinical features, management, and prognosis

Immune checkpoint inhibitor-related diabetes mellitus (ICI-DM) is a rare complication that medical oncologists seldom encounter in routine practice. The sporadic nature and intrinsic complexity of ICI-DM make it challenging to analyze comprehensively in experimental settings. In this review, we examine phase 3 clinical trials on ICIs and published case reports of ICI-DM, aiming to summarize its incidence, clinical features, management, and prognosis. Phase 3 clinical trials reveal that the incidence of ICI-DM is higher with combination therapies, such as anti-PD-1 and anti-CTLA-4 or anti-PD-L1, compared to anti-PD-1 monotherapy. ICI-DM typically presents as severe hyperglycemia with a fulminant onset and is often associated with diabetic ketoacidosis, accompanied by unexpectedly low HbA1c and C-peptide levels. ICI-DM shares similarities with classic type 1 diabetes, particularly in terms of autoimmunity and genetic predisposition. This includes a high prevalence of islet autoantibodies and susceptibility to certain HLA haplotypes, often with concurrent endocrine gland dysfunction. This suggests that genetic susceptibility and exposure to ICIs may both be necessary for triggering islet autoimmunity and inducing ICI-DM. Notably, patients with positive islet autoantibodies, such as glutamic acid decarboxylase antibody and islet-associated antigen 2 antibody, tend to experience rapid onset of ICI-DM after ICI exposure. Although patients with ICI-DM generally show a high objective response rate to immunotherapy, a significant proportion also face the need to permanently discontinued treatment. Further research is urgently needed to determine whether permanent discontinuation of immunotherapy is necessary and whether this discontinuation negatively impacts overall survival.

Sustainable photocatalytic hydrogen peroxide production over octonary high-entropy oxide

The direct utilization of solar energy for the artificial photosynthesis of hydrogen peroxide (H2O2) provides a reliable approach for producing this high-value green oxidant. Here we report on the utility of high-entropy oxide (HEO) semiconductor as an all-in-one photocatalyst for visible light-driven H2O2 production directly from H2O and atmospheric O2 without the need of any additional cocatalysts or sacrificial agents. This high-entropy photocatalyst contains eight earth-abundant metal elements (Ti/V/Cr/Nb/Mo/W/Al/Cu) homogeneously arranged within a single rutile phase, and the intrinsic chemical complexity along with the presence of a high density of oxygen vacancies endow high-entropy photocatalyst with distinct broadband light harvesting capability. An efficient H2O2 production rate with an apparent quantum yield of 38.8% at 550 nm can be achieved. The high-entropy photocatalyst can be readily assembled into floating artificial leaves for sustained on-site production of H2O2 from open water resources under natural sunlight irradiation.

The principle of compromise-in-competition: understanding mesoscale complexity of different levels

This perspective is not meant to be a complete summary of the full series of our studies over decades, rather it aims to look back across the entire process to outline: What inspired this work at its beginning? How did the mindset gradually evolve from a solely engineering study to the new concept of Mesoscience? And where is the research currently being focused and to be extended? This approach elucidates the central importance of correlating precise engineering problems to be resolved with associated missing links in the underlying, fundamental science while exploring a notable commonality, namely, the universality of Mesoscience. Inspired by the physical phenomenon of the coexistence and interaction between a gas-rich dilute phase and a solid-rich dense phase with different physical mechanisms dominating the behaviour for each phase in gas–solid fluidization, it was recognized that an underlying compromise-in-competition (CIC) exists between these two physically dominant mechanisms. This, then, is the CIC principle. We believe that this is the origin of inherent structural complexity in these and other systems. We have shown that this CIC principle can be formulated as a multi-objective optimization problem through the so-called energy-minimization multi-scale model, leading to a massive improvement in both the accuracy and scalability of computation and its wide application in both academia and industries. Furthermore, this approach was extended to formulate and understand other complex systems in many apparently disparate fields, indicating the wide applicability of the CIC principle and then resulting in the proposition and advancement of the concept of Mesoscience. Furthermore, within the main body of this perspective article, future directions for identifying, developing and applying the concept of Mesoscience will be prospected. This includes extending its possible generality and applications in many different disciplines and fields, even to analysing global challenging issues and promoting CIC-informed AI. In this way, many challenging problems in engineering—identified through their underlying mesoscale complexity—will be closely correlated with the development of future fundamental science. Through this approach, we hope to illustrate that fundamental research can now tackle the intrinsic complexity existing in a multitude of engineering problems. Through the concept of Mesoscience, we aim to explore the possible commonality from complexity. The deduction of commonality from diversity is possible in resolving global challenges, shifting paradigms in science and filling in the existing gaps at the mesoscales of different levels of our knowledge, though there are difficulties and uncertainty coming from diversity.

In defense of obscure academic writing

There has been a backlash against academic writing in the humanities that can be found in popular culture at least since the 1990s. By considering a selection of arguments from critics of academic writing in the humanities, I then defend a certain kind of ‘obscurity’ or ‘difficulty’ in scholarly prose by developing a concise genealogy of obscure academic writing. This paper develops the notion that an ‘uncommon sense’ should be situated against ‘common sense’ to unveil an assumed ‘world’ or ‘reality’ alongside the structural formations of that which is considered ‘useful’ language. Often, complicated academic language grapples with the intrinsic complexity that is hidden behind ‘simple’ ideas.

Extending the computational reach of a superconducting qutrit processor

Quantum computing with qudits is an emerging approach that exploits a larger, more connected computational space, providing advantages for many applications, including quantum simulation and quantum error correction. Nonetheless, qudits are typically afflicted by more complex errors and suffer greater noise sensitivity which renders their scaling difficult. In this work, we introduce techniques to tailor arbitrary qudit Markovian noise to stochastic Weyl–Heisenberg channels and mitigate noise that commutes with our Clifford and universal two-qudit gate in generic qudit circuits. We experimentally demonstrate these methods on a superconducting transmon qutrit processor, and benchmark their effectiveness for multipartite qutrit entanglement and random circuit sampling, obtaining up to 3× improvement in our results. To the best of our knowledge, this constitutes the first-ever error mitigation experiment performed on qutrits. Our work shows that despite the intrinsic complexity of manipulating higher-dimensional quantum systems, noise tailoring and error mitigation can significantly extend the computational reach of today’s qudit processors.

The Structure of Field‐Aligned Current Systems as Inferred From the Multiscale Minimum Variance Analysis

AbstractAuroral field‐aligned currents (FACs) have an intrinsic complexity caused by the superposition of contributions from a broad spectrum of scales and diversity of locations. The complex FAC systems are investigated by using the multiscale minimum variance analysis. This technique provides a scale based decomposition of the FAC systems by identifying the constituting FAC elements as well as their structure. At the basis, the analysis exploits the scale dependence of the eigenvalues of the magnetic field variance matrix. The scale decomposition along the transversal (latitudinal) direction results from the scale derivative of the maximum eigenvalue. The complementary information from the scale derivative of the minimum eigenvalue helps to infer the full structure of each FAC element by providing estimates of the FAC length (longitudinal) scale. The scale derivative of minimum and maximum eigenvalues are used to identify FAC signatures associated to different types of aurora (e.g., highly extended, finite arcs, gradient regions) as well as to characterize the influence of the crossing location with respect to the FAC structures (e.g., near edge crossings). The multiscale analysis is applied to simulated FACs and to spacecraft observations made by the Swarm mission. The use with real world data illustrates the power of this analysis, whose full benefits for magnetosphere‐ionosphere coupling investigations are yet to be explored.

Deep transformer-based architecture for the recognition of mathematical equations from real-world math problems

Identifying mathematical equations from real-world math problems presents a unique and challenging task within the field of Natural Language Processing (NLP). It has a wide range of applications in various areas, such as academics, digital content design, and the development of automatic or interactive learning systems. However, the accurate understanding of these equations still needs to be enhanced due to the intrinsic complexity of mathematical symbols and various structural formats. Additionally, the unique syntax, diverse symbols, and complex structure of mathematical equations present significant obstacles that traditional NLP methods and Optical Character Recognition (OCR) systems need help to overcome. In this research, we utilize deep transformer architecture to recognize mathematical equations, and we have utilized our novel dataset with 3433 distinct observations. This dataset, which we have collected to include a diverse range of mathematical equations, is used to predict six (z=x+y, z=x−y, z=xy, z=x/y, y=x! and y=x) basic mathematical equations. We applied different transformer-based architectures, such as BERT, ELECTRA, XLNet, RoBERTa, and DistilBERT, and BERT performs best with 99.80% accuracy. To the best of our knowledge, this is the first NLP work in any language where we recognize the equation from the mathematical text.

Continuation of nonlinear normal modes using reduced-order models based on generalized characteristic value decomposition

AbstractOver the past two decades, data-driven reduced-order modeling (ROM) strategies have gained significant traction in the nonlinear dynamics community. Currently, several challenges in physical interpretation and data availability remain overlooked in current methodologies. This work proposes a novel ROM methodology based on a newly proposed generalized characteristic value decomposition (GCVD) to address these obstacles. The GCVD-ROM approach proposes a new perspective toward data-driven ROMs via characterization of the dynamics before any ROM considerations are made. In doing so, a significant degree of versatility is inherited in the GCVD-ROM strategy, allowing our models to reproduce the full-scale dynamics in different regions of the parameter space at the cost of a single training data set. Our approach utilizes computationally efficient free-decay data sets alongside a windowed-decomposition scheme, allowing us to extract energy-dependent modal structures for use in model-order reduction. This is accomplished using the physically insightful characteristic values provided by the GCVD, which are shown to be directly related to the system poles at a particular response amplitude. This natural metric, paired with a resonance tracking scheme, allows us to address the difficulties associated with physical interpretation and data availability without sacrificing the convenient aspects of linear projection-based model order reduction. A computational framework for the continuation and bifurcation analysis using linear projection-based ROMs is also presented, permitting us to deploy rigorous analysis and bifurcation studies to verify that our ROMs reproduce the intrinsic complexity of full-scale systems. A detailed walk-through of the GCVD-ROM approach is demonstrated on a simple system where important practical considerations and implementation details are discussed using a concrete example. The discretized von Kármán beam and shallow arch partial differential equations are also used to explore complicated scenarios involving modal coupling across disparate time scales and internal resonances.

Enhancing Deep Learning-Based City-Wide Traffic Prediction Pipelines Through Complexity Analysis

Deep learning models can effectively capture the non-linear spatiotemporal dynamics of city-wide traffic forecasting. Evidence of varying deep learning model performance between different cities, different prediction horizons, different scales, specific city regions, and during particular hours of the day abounds in the literature on deep learning-based traffic prediction, yet a unified metric to quantify the complexity of different prediction tasks does not exist. This paper proposes two metrics—model complexity (MC) and intrinsic complexity (IC). While MC quantifies the effective complexity of deep learning models for city-wide traffic prediction tasks, the IC quantifies the underlying complexity of the prediction task. Being an effective complexity metric, MC depends on the model and the data. The IC depends only on the data and is invariant to the model being used. Both metrics are validated through systematic experimentation using traffic volume data from three cities. Finally, we demonstrate how these metrics can improve the workflows for deep learning-based data-driven traffic prediction pipelines and deployment by reducing the hyperparameter search scope and comparing the effectiveness of different design pathways.

Obfuscation strategies for industrial control systems

Recently released scan data on Shodan reveals that thousands of Industrial Control Systems (ICSs) worldwide are directly accessible via the Internet and, thus, exposed to cyber-attacks aiming at financial gain, espionage, or disruption and/or sabotage. Executing sophisticated cyber–physical attacks aiming to manipulate industrial functionalities requires a deep understanding of the underlying physical process at the core of the target ICS, for instance, through unauthorized access to memory registers of Programmable Logic Controllers (PLCs). However, to date, countermeasures aiming at hindering the comprehension of physical processes remain largely unexplored.In this work, we investigate the use of obfuscation strategies to complicate process comprehension of ICSs while preserving their runtime evolution. To this end, we propose a framework to design and evaluate obfuscation strategies for PLCs, involving PLC memory registers, PLC code (user program), and the introduction of extra (spurious) physical processes. Our framework categorizes obfuscation strategies based on two dimensions: the type of (spurious) registers employed in the obfuscation strategy and the dependence on the (genuine) physical process. To evaluate the efficacy of proposed obfuscation strategies, we introduce evaluation metrics to assess their potency and resilience, in terms of system invariants the attacker can derive, and their cost in terms of computational overhead due to runtime modifications of spurious PLC registers. We developed a prototype tool to automatize the devised obfuscation strategies and applied them to a non-trivial use case in the field of water tank systems. Our results show that code obfuscation can be effectively used to counter malicious process comprehension of ICSs achieved via scanning of PLC memory registers. To our knowledge, this is the first work using obfuscation as a technique to protect ICSs from such threats. The efficacy of the proposed obfuscation strategies predominantly depends on the intrinsic complexity of the interplay introduced between genuine and spurious registers.

Buffering Donor Shuttles in Proton-Coupled Electron Transfer Kinetics for Electrochemical Hydrogenation of Hydroxyacetone to Propylene Glycol.

Electrochemical hydrogenation reactions demand rapid proton-coupled electron transfer at the electrode surface, the kinetics of which depend closely on pH. Buffer electrolytes are extensively employed to regulate pH over a wide range. However, the specific role of buffer species should be taken into account when interpreting the intrinsic pH dependence, which is easily overlooked in the current research. Herein, we report the electrochemical hydrogenation of hydroxyacetone, derived from glycerol feedstock, to propylene glycol with a faradaic efficiency of 56 ± 5% on a polycrystalline Cu electrode. The reaction activities are comparable in citrate, phosphate, and borate buffer electrolytes, encompassing different buffer identities and pH. The electrokinetic profile reveals that citrate is a site-blocking adsorbate on the Cu surface, thereby decreasing buffer concentration and increasing pH will enhance the reaction rate; phosphate is an explicit proton donor, which promotes the interfacial rate by increasing buffer concentration and decreasing pH, while borate is an innocent buffer, which can be used to investigate the intrinsic pH effect. Combined with in situ SEIRAS, we demonstrate that water is the primary proton source in citrate and borate electrolytes, reiterating the rationality of the proposed mechanism based on the microkinetic modeling. Our results emphasize the intrinsic complexity of the buffer system on the kinetic activity for electrocatalysis. It calls for special care when we diagnose the mechanistic pathway in buffer electrolytes convoluted by different buffer identities and pH.

MolPackL: Quantification and Interpretation of Intermolecular Interactions Driven by Molecular Packing.

In organic optoelectronic devices, the properties of the aggregated organic materials depend not only on individual molecules or monomers but also significantly on their packing modes. Different from their inorganic counterparts linked by explicit covalent bonds, organic solids exhibit intricate and numerous intermolecular interactions (IMIs). Due to the intrinsic complexity and disorder of IMIs, identifying and understanding them is a formidable challenge in experimental, theoretical, and data-driven approaches. In this work, we constructed an innovative algorithm framework, Molecular Packing Learning (MolPackL), which can accurately quantify elusive IMIs using contact density histograms (CDHs) and efficiently extract intermolecular features for further property prediction of organic solids. It performs satisfactorily in training predictive models of IMI-related properties in molecular crystals. Particularly, the band gap predictive model based on MolPackL achieved the best-reported performance, with an MAE of 0.20 eV and an impressive R2 of 0.92. Class activation mapping (CAM) visually demonstrates MolPackL's accurate identification of effective interaction sites as the molecular packing changes. What is more, the elemental importance analysis verified that the superior score benefits from MolPackL's ability to comprehensively consider multiple influencing factors of IMIs. In summary, MolPackL provides a new framework for quantitative assessment and understanding of the effect of IMIs. The development of MolPackL marks a significant advancement in establishing predictive models of molecular aggregates, deepening the comprehension of IMIs on the material properties. Given the superior performance, we believe that MolPackL will also become a powerful tool in the design of high-performance organic optoelectronic materials.

A hybrid approach to analysing large scale surveys: individual values, opinions and perceptions

High-dimensional large scale surveys enable broad research capabilities and potential insight. However, when dealing with the intrinsic complexity of social science, the underlying knowledge engineering process may play a critical role and require to consider the characteristics and peculiarities of a given problem in context. This study proposes an analysis framework based on clustering techniques, which have been applied to discover patterns among a number of abstracted features resulting from selected attributes of the World Values Survey (WVS). As an assumption, such features have been softly classified as values, opinions and perceptions, based on their theoretical likelihood to change along the time. From a more philosophical perspective, this work assumes hybrid practices as there is no pre-formulated theory but rather an attempt to discover patterns and new knowledge from data. Given the relatively manageable dimensionality of the input dataset, the feature selection has been performed according to an application-oriented approach (rather than driven by statistical analysis) to establish a more comprehensive and consistent research framework. Among the main findings, a symbiotic relationship between the perception of satisfaction and of financial stability, as well as between the perception of security and of happiness, in addition to more complex patterns involving traditional values (e.g. family and religion), politics and believes with an impact on society. Last but not least, despite its holistic focus, the study has allowed the identification of few research gaps and, therefore, potential further research direction in the broad domain of Social Sciences.

MapReduce algorithms for robust center-based clustering in doubling metrics

Clustering is a pivotal primitive for unsupervised learning and data analysis. A popular variant is the (k,ℓ)-clustering problem, where, given a pointset P from a metric space, one must determine a subset S of k centers minimizing the sum of the ℓ-th powers of the distances of points in P from their closest centers. This formulation covers the well-studied k-median (ℓ=1) and k-means (ℓ=2) clustering problems. A more general variant, introduced to deal with noisy pointsets, features a further parameter z and allows up to z points of P (outliers) to be disregarded when computing the sum. We present a distributed coreset-based 3-round approximation algorithm for the (k,ℓ)-clustering problem with z outliers, using MapReduce as a computational model. An important feature of our algorithm is that it obliviously adapts to the intrinsic complexity of the dataset, captured by its doubling dimension D. Remarkably, for D=O(1), our algorithm requires sublinear local memory per reducer, and yields a solution whose approximation ratio is an additive term O(γ) away from the one achievable by the best known sequential (possibly bicriteria) algorithm, where γ can be made arbitrarily small. To the best of our knowledge, no previous distributed approaches were able to attain similar quality-performance tradeoffs for metrics with constant doubling dimension.

Quantifying city freight mobility segregation associated with truck multi-tours behavior

Freight mobility segregation, a phenomenon like residential social segregation, is a crucial aspect of city landscapes that influences the livability and sustainability of cities. However, there is a deficiency in understanding the intrinsic complexity of freight mobility segregation, particularly regarding the micro-level truck behaviors. In this study, we develop a new approach to assess the degree of freight mobility segregation within cities by leveraging large-scale truck GPS data in Chinese cities. The analysis indicates the existence of freight mobility segregation in cities, where certain groups of trucks serve high-demand areas, while another group of trucks serves low-demand areas. The activity spaces of distinct truck groups are largely non-overlapping or segregated. To uncover the correlations between mobility segregation and truck operational patterns, we introduce two metrics to characterize truck multi-tours behavior, focusing on tour pattern predictability and activity explorability. By employing freight point-of-interest (POI) data, we further reveal the influence of local economic structures and industrial compositions on mobility segregation in cities. These findings enrich our understanding of the dynamics of city freight systems, offering implications for improving logistics efficiency and fostering sustainable city development.

Production of an introperative safety list in interventional cardiology. From pilot project to clinical practice guidelines hospital

Abstract Introduction The safety list was created to respond to two complexities: the first is the fallibility of human memory and attention; The second is the tendency to skip some operations even when remembering that they should be performed: not all operations in the supply chain of a process are always considered important so the shortcut is taken, motivated by the fact that nothing has ever happened so far. Safety in Interventional Cardiology laboratories is characterized by the intrinsic complexity that characterizes all interventional procedures, even the simplest ones: complexity due to the number of people and professionals involved, the acute and/or serious conditions of the patients, the amount of information required, the urgency with which the processes must be performed, the high technological and high level of specialization and the multiplicity of critical points of the processes that can cause serious harm to patients. For this reason, an intraoperative safety list for interventional cardiology has been designed and drawn up. Aim In view of Law no. 24 of 8 March 2017, a provision on the safety of care and the assisted person, it was necessary to have a document aimed at: improving the quality and safety of surgical interventions and interventional/structural procedures, dissemination of recommendations and safety standards aimed at strengthening pre- intra and post-operative processes, Implementation of safety standards through the application of the safety list, adapting it to the characteristics of your organization. Materials and methods The trial phase began in 2021 and was tested on about 800 patients, in 2022 on about 900 patients. The safety list has been designed to combine an intraoperative "check list" with a "nursing card", collecting information relating to the individual people assisted, in order to promote continuity and personalization of care, represents a resource for research on nursing care, for the analysis of care complexity and allows the traceability of the actions carried out. Results and conclusions After years of constant reviews and updates with the collaboration of the Risk Management and Quality staff, the safety list has become a fundamental part of the work of the operating room, supporting both system and individual changes, strengthening standards for safety and communication processes, counteracting possible failure factors, having been included in the hospital Guidelines on the management of patients undergoing surgery.

NXRouting: A GPU-Enhanced CAD Tool for European Radiation-Hardened FPGAs

Field Programmable Gate Arrays (FPGAs) have witnessed an increase in space applications in the last years, mainly due to their cost-effective high-performances and flexibility. However, the susceptibility of these devices to radiation-induced effects when working in such an environment is well known. When common mitigation techniques are not sufficient to ensure the correct completion of a task, radiation-hardened FPGAs represent one of the most effective solutions. NanoXplore, in this context, is the first European developer of rad-hard FPGAs, which embed intrinsic high complexity in their architectures preventing the user from using or developing custom placement and routing algorithms. In this paper, we overcame these issues by proposing the first tool tailored to NanoXplore devices which allows the exploration of NanoXplore device architectures and routing of points through a Python interface. We developed a model that reflects the one used by the vendor, allowing the user to extract info about routes, nets and additional logic, otherwise unavailable. The tool also performs routing of points in the programmable logic, computing the optimal path. An implementation of the router on Graphic Processing Unit (GPU) is proposed to exploit the highly parallelizable nature of the problem. Finally, routing timing analyses on different benchmarks have been performed, improving the routing routine time.

Coupled X-ray Absorption/UV-vis Monitoring of a Prototypical Oscillating Reaction.

Oscillating reactions are among the most intriguing phenomena in chemistry, but many questions on their mechanisms still remain unanswered, due to their intrinsic complexity and to the low sensitivity of the most common spectroscopic techniques toward the reaction brominated species. In this work, we investigate the cerium ion-catalyzed Belousov-Zhabotinsky (BZ) oscillating reaction by means of time-resolved X-ray absorption spectroscopy (XAS), in combination with UV-vis spectroscopy and unsupervised machine learning, multivariate curve resolution, and kinetic analyses. Altogether, we provide new insights into the collective oscillatory behavior of the key brominated species involved in the classical BZ reaction and measure previously unreported oscillations in their concentrations through Br K-edge XAS, while simultaneously tracking the oscillatory Ce4+-to-Ce3+ transformation by coupling XAS with UV-vis spectroscopy. Our work evidences the potential of the XAS technique to investigate the mechanisms of oscillatory chemical systems whose species are often not detectable with conventional experimental methods.